Part 2

2. FBW
26-02-2019
Biological Databases
Wim Van Criekinge

4. Familienaam Voornaam E-mail
Anhel Valdes Ana-MariyaAnaMariya.AnhelValdes@UGent.be
De Waele Gaetan Gaetan.DeWaele@UGent.be
De Nolf Melanie Melanie.DeNolf@UGent.be
de Fooz Nicolas Nicolas.deFooz@UGent.be
Tavernier Simon Simon.Tavernier@UGent.be
Deschildre Joke Joke.Deschildre@UGent.be
Duarri Redondo Sara Sara.DuarriRedondo@UGent.be
Opdebeeck Helder Helder.Opdebeeck@UGent.be
Voorthuijzen Floris Floris.Voorthuijzen@UGent.be
Van Hoyweghen Lennert Lennert.VanHoyweghen@UGent.be
Ozcan Umut Onur UmutOnur.Ozcan@UGent.be
Gao Duan Duan.Gao@UGent.be
Dierickx Sam Sam.Dierickx@UGent.be
Philips Gino Gino.Philips@UGent.be
Juan Cabot Aina Aina.JuanCabot@UGent.be
De Saedeleer Bianca Bianca.DeSaedeleer@UGent.be
Pollaris Lotte Lotte.Pollaris@UGent.be
Li Qianqian Qianqian.Li@UGent.be

5. Officiële code Familienaam Voornaam E-mail Opleidingsprogramma
101803794 Anhel Valdes Ana-Mariya AnaMariya.AnhelValdes@UGent.be IXGAEX
201814118 Bergman Anna Anna.Bergman@UGent.be IXGAEX
301506429 de Fooz Nicolas Nicolas.deFooz@UGent.be IMCELB
401509165 De Nolf Melanie Melanie.DeNolf@UGent.be CMBIOISB
501409888 De Saedeleer Bianca Bianca.DeSaedeleer@UGent.be CMBIOIBE
601503275 Deschildre Joke Joke.Deschildre@UGent.be CMBIOIBE
701503542 De Waele Gaetan Gaetan.DeWaele@UGent.be CMBIOIBE
801804175 Dierickx Sam Sam.Dierickx@UGent.be CMBIOIBE
901802644 Duarri Redondo Sara Sara.DuarriRedondo@UGent.be CXGAEX
1001716008 Gao Duan Duan.Gao@UGent.be CMBIOISB
1101811008 Juan Cabot Aina Aina.JuanCabot@UGent.be CMBIOISB
1201400344 Langhendries Milan Milan.Langhendries@UGent.be CMBIOISB
1301804233 Li Qianqian Qianqian.Li@UGent.be CMBIOISB
14 20044839Opdebeeck Helder Helder.Opdebeeck@UGent.be CMBIOISB
1501801850 Ozcan Umut Onur UmutOnur.Ozcan@UGent.be CMBIOISB
1601206178 Philips Gino Gino.Philips@UGent.be CMBIOISB
1701509837 Pollaris Lotte Lotte.Pollaris@UGent.be CMBIOIBE
1801403113 Tavernier Simon Simon.Tavernier@UGent.be CMBIOISB
1901500186 Van Hoyweghen Lennert Lennert.VanHoyweghen@UGent.be CMBIOIBE
2001503491 Voorthuijzen Floris Floris.Voorthuijzen@UGent.be CMBIOIBE

7. Big Data Volumes

9. Data Levels in Biological Research

10. Primary dataDerived data

11. Primary dataDerived data
Interpreted data/
knowledge
Experimental metadata
Analytical metadata

12. Direct Queryability of Selected Bioinformatics Databases
Database Direct Querying? How? Modality DB Engine
ArrayExpressWarehouse Eventually http://www.ebi.ac.uk/aedw/ SQL Oracle
BioWarehouse Yes
http://biowarehouse.ai.sri.com/
- need account
SQL Oracle/MySQL
Ensembl Yes
http://www.ensembl.org/info/d
ata/download.html
SQL MySQL
Mouse Genome Database Yes ask for account SQL Sybase
PharmGKB Yes
http://www.pharmgkb.org/hom
e/projects/webservices/
SOAP-based Oracle
Saccharomyces Genome
Database
EventuallyMaybe Oracle
Stanford Microarray Database No Oracle

13. Example 3-tier model in biological database

14. Rationale to study SQL
• Learning SQL to Query Biological Databases
• Are you getting too much data, or missing crucial
data when querying via a Web interface? Many
biological databases can be queried directly via
SQL, thus bypassing the limitations of the
database's interface. SQL is at the heart of
biological databases as diverse as Ensembl,
ArrayExpress and the Mouse Genome Database.
• Focus on understanding relational databases by
studying a repository of biological data and learn
how to query it using SQL.

15. SQL consists of only 4 statements, sometimes
referred to as CRUD:
–Create - INSERT - to store new data
–Read - SELECT - to retrieve data
–Update - UPDATE - to change or modify
data.
–Delete - DELETE - delete or remove data
Structured Query Language

16. Definitions
Definitions
• Database: Collection of tables 
• Table
– Collection of records that share a common
fundamental characteristic
• E.g., patients and locations can each be stored in
their own table
• Record
– Basic unit of information in a relation
information
– A record is composed of fields
– E.g., 1 record per person
• Query
– Set of instructions to a database “engine” to
retrieve, sort and format returning data.
• “find me all patients in my database”

17. Table Characteristics
• Two-dimensional structure with rows and
columns
• Rows (tuples) represent single entity
• Columns represent attributes
• Row/column intersection represents single value
• Tables must have an attribute to uniquely identify
each row
• Column values all have same data format
• Each column has range of values called attribute
domain
• Order of the rows and columns is immaterial to
the DBMS

18. Numeric Data Types
MySQL uses all the standard ANSI SQL numeric data types, so if you're coming to MySQL from a different
database system, these definitions will look familiar to you. The following list shows the common numeric data
types and their descriptions:
INT - A normal-sized integer that can be signed or unsigned. If signed, the allowable range is from -2147483648 to
2147483647. If unsigned, the allowable range is from 0 to 4294967295. You can specify a width of up to 11 digits.
TINYINT - A very small integer that can be signed or unsigned. If signed, the allowable range is from -128 to 127.
If unsigned, the allowable range is from 0 to 255. You can specify a width of up to 4 digits.
SMALLINT - A small integer that can be signed or unsigned. If signed, the allowable range is from -32768 to
32767. If unsigned, the allowable range is from 0 to 65535. You can specify a width of up to 5 digits.
MEDIUMINT - A medium-sized integer that can be signed or unsigned. If signed, the allowable range is from -
8388608 to 8388607. If unsigned, the allowable range is from 0 to 16777215. You can specify a width of up to 9
digits.
BIGINT - A large integer that can be signed or unsigned. If signed, the allowable range is from -
9223372036854775808 to 9223372036854775807. If unsigned, the allowable range is from 0 to
18446744073709551615. You can specify a width of up to 20 digits.
FLOAT(M,D) - A floating-point number that cannot be unsigned. You can define the display length (M) and the
number of decimals (D). This is not required and will default to 10,2, where 2 is the number of decimals and 10 is
the total number of digits (including decimals). Decimal precision can go to 24 places for a FLOAT.
DOUBLE(M,D) - A double precision floating-point number that cannot be unsigned. You can define the display
length (M) and the number of decimals (D). This is not required and will default to 16,4, where 4 is the number of
decimals. Decimal precision can go to 53 places for a DOUBLE. REAL is a synonym for DOUBLE.
DECIMAL(M,D) - An unpacked floating-point number that cannot be unsigned. In unpacked decimals, each
decimal corresponds to one byte. Defining the display length (M) and the number of decimals (D) is required.
NUMERIC is a synonym for DECIMAL.

19. String Data Type
• Although numeric and date types are fun, most data you'll store will be in string format. This list describes
the common string datatypes in MySQL.
• CHAR(M) - A fixed-length string between 1 and 255 characters in length (for example CHAR(5)), right-
padded with spaces to the specified length when stored. Defining a length is not required, but the default
is 1.
• VARCHAR(M) - A variable-length string between 1 and 255 characters in length; for example
VARCHAR(25). You must define a length when creating a VARCHAR field.
• BLOB or TEXT - A field with a maximum length of 65535 characters. BLOBs are "Binary Large Objects"
and are used to store large amounts of binary data, such as images or other types of files. Fields defined
as TEXT also hold large amounts of data; the difference between the two is that sorts and comparisons
on stored data are case sensitive on BLOBs and are not case sensitive in TEXT fields. You do not specify
a length with BLOB or TEXT.
• TINYBLOB or TINYTEXT - A BLOB or TEXT column with a maximum length of 255 characters. You do
not specify a length with TINYBLOB or TINYTEXT.
• MEDIUMBLOB or MEDIUMTEXT - A BLOB or TEXT column with a maximum length of 16777215
characters. You do not specify a length with MEDIUMBLOB or MEDIUMTEXT.
• LONGBLOB or LONGTEXT - A BLOB or TEXT column with a maximum length of 4294967295
characters. You do not specify a length with LONGBLOB or LONGTEXT.
• ENUM - An enumeration, which is a fancy term for list. When defining an ENUM, you are creating a list of
items from which the value must be selected (or it can be NULL). For example, if you wanted your field to
contain "A" or "B" or "C", you would define your ENUM as ENUM ('A', 'B', 'C') and only those values (or
NULL) could ever populate that field.

20. Built-in Data Types in SQL
• date: Dates, containing a (4 digit) year, month and
date
– Example: date ‘2005-7-27’
• time: Time of day, in hours, minutes and seconds.
– Example: time ‘09:00:30’ time ‘09:00:30.75’
• timestamp: date plus time of day
– Example: timestamp ‘2005-7-27 09:00:30.75’
• interval: period of time
– Example: interval ‘1’ day
– Subtracting a date/time/timestamp value from another gives
an interval value
– Interval values can be added to date/time/timestamp values

21. Build-in Data Types in SQL (Cont.)
• Can extract values of individual fields from
date/time/timestamp
– Example: extract (year from r.starttime)
• Can cast string types to
date/time/timestamp
– Example: cast <string-valued-expression>
as date
– Example: cast <string-valued-expression>
as time

22. User-Defined Types
• create type construct in SQL creates user-defined
type
create type Dollars as numeric (12,2) final
• create domain construct in SQL-92 creates user-
defined domain types
create domain person_name char(20) not null
• Types and domains are similar. Domains can have
constraints, such as not null, specified on them.

23. Large-Object Types
• Large objects (photos, videos, CAD files, etc.)
are stored as a large object:
– blob: binary large object -- object is a large
collection of uninterpreted binary data (whose
interpretation is left to an application outside of
the database system)
– clob: character large object -- object is a large
collection of character data
– When a query returns a large object, a pointer is
returned rather than the large object itself.

24. Creating a Table
• To create a table, use the CREATE TABLE command:
mysql> CREATE TABLE pet (
-> name VARCHAR(20),
-> owner VARCHAR(20),
-> species VARCHAR(20),
-> sex CHAR(1),
-> birth DATE, death DATE);
Query OK, 0 rows affected (0.04 sec)

25. Keys
• One or more attributes that
determine other attributes
– Key attribute
– Composite key
• Full functional dependence
• Entity integrity
– Uniqueness
– No ‘null’ value in key

26. Example Tables

27. Simple Relational Database

28. Keys (con’t.)
• Primary key
– Candidate key to uniquely identify all other
attributes in a given row
• Foreign key
– Values must match primary key in another
table

29. Integrity Rules
• Entity integrity
– Ensures all entities are unique
– Each entity has unique key
• Referential integrity
– Foreign key must have null value or match
primary key values
– Makes it impossible to delete row whose
primary key has mandatory matching foreign
key values in another table

30. Integrity Constraints
• Integrity constraints guard against
accidental damage to the database, by
ensuring that authorized changes to the
database do not result in a loss of data
consistency.
– A checking account must have a balance
greater than $10,000.00
– A salary of a bank employee must be at
least $4.00 an hour
– A customer must have a (non-null) phone
number

31. Referential Integrity
• Ensures that a value that appears in one relation for a given set of
attributes also appears for a certain set of attributes in another
relation.
– Example: If “Perryridge” is a branch name appearing in one of the
tuples in the account relation, then there exists a tuple in the
branch relation for branch “Perryridge”.
• Primary and candidate keys and foreign keys can be specified as part
of the SQL create table statement:
– The primary key clause lists attributes that comprise the primary
key.
– The unique key clause lists attributes that comprise a candidate
key.
– The foreign key clause lists the attributes that comprise the
foreign key and the name of the relation referenced by the foreign
key. By default, a foreign key references the primary key attributes
of the referenced table.

32. Relationships within Relational Database
• Relationship classifications
– 1:1
– 1:M
– M:N
• E-R Model
– ERD Maps E-R model
– Entities - of tabellen. Ze worden weergegeven door
rechthoeken met daarin de naam van het
entiteitstype en soms een opsomming van de
attributen
– Relationships - dit zijn verbanden tussen de
entiteittypen. We worden weergegeven door lijnen
tussen de verbonden eniteitstypen

33. ERD Symbols
• Rectangles represent entities
• Diamonds represent the relationship(s) between
the entities
• “1” side of relationship
– Number 1 in Chen Model
– Bar crossing line in Crow’s Feet Model
• “Many” relationships
– Letter “M” and “N” in Chen Model
– Three pronged “Crow’s foot” in Crow’s Feet Model

34. Example 1:M Relationship

35. Example 1:M Relationship

36. Example M:N Relationship

37. Example M:N Relationship

38. Converting M:N Relationship to Two 1:M Relationships

39. Converting M:N Relationship to Two 1:M Relationships (con’t.)

40. Converting M:N Relationship to Two 1:M Relationships (con’t.)

41. Converting M:N Relationship to Two 1:M Relationships (con’t.)

42. Indexes
• Points to location
• Makes retrieval of data faster

43. Applications of B+-Trees
(1) Search key of B+-Tree is primary key for data file, index
is dense (data file may or may not be sorted by pk):
– There is one key-pointer pair in a leaf for every
record of the data file
(2) Data file is sorted by its primary key:
– B+-Tree is a sparse index with one key-pointer
pair at a leaf for each block of the file
(3) Data file is sorted by an attribute that is not a key
(search key for B+-Tree):
– For each value K that appears in the data file,
there is one key-pointer pair at the leaf.
Pointer goes to the first of the records that
have K as their sort-key value

54. Typical ODBC Architecture

55. How ODBC Works?
• ODBC inserts a middle layer called a Client
Driver
• Purpose of the Client Driver is to translate the
applications queries into commands that the
DBMS understands

56. Other Parts of the Architecture
• Application – calls functions defined in the
ODBC API to access a data source
• Driver Manager – implements the ODBC API
and provides information to the application
• Database – contains all the data

57. Setting Up a Data Source in Windows
• A data source is just a database that ODBC
connects to
• This allows the person to change database
types without any changes to the program
• Step 1. Get a database.
– Using access for this example because it’s on this
computer

58. Conclusion
The four parts that make up ODBC:

59. Conclusion
• Advantages:
– Allows access to different types of databases
– Uniform way of retrieving information
– Highly efficient
– Low memory requirements
• Disadvantages
– Complex and steep learning curve
– All data in database must look like a relational
database

60. Conclusion
• ODBC changed the way people code their programs
that have to interact with databases.
• The efficiency of programmers in the business world
has increased due to ODBC because they no longer
are wasting time to create multiple copies of a
program.
• ODBC has vastly improved the way programmers
deal with databases.

65. Application/utilities

66. Toad Edge for MySQL

67. Install BIOSQL locally
• Get latest version of mysql (MAMP,
mariaDB)
• Download biosqldb-mysql.sql
• Remove type=innodb
• Launch database server
• Connect using toad (port 8889)
• Create database biosql;
• Set as active database
• Use worksheet to execute biosqldb-
mysql.sql

68. Project